Instructions to use Mirkat/Plant_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mirkat/Plant_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mirkat/Plant_Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Mirkat/Plant_Classification") model = AutoModelForImageClassification.from_pretrained("Mirkat/Plant_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad9be74e4b19ce080b1514b013953fdea307be3d9959e16d98b78e3caeba77a9
- Size of remote file:
- 343 MB
- SHA256:
- a6c092d7c5b9f453b9c06ae90b5869ac471bb7a110aee92da14819d546852281
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